June 2024 / Lugano / Switzerland
The primary challenge in modeling body motion and emotion lies in effectively capturing the complex interplay of these factors in a structured and controllable manner. Traditional VAE models often struggle to generate diverse and expressive samples, particularly when the dataset is limited in capturing the full range of emotional expressions and motion variations. The integration of multiple components such as PSO, fuzzy logic, hierarchical structure, conditional inputs, and disentanglement seeks to address these challenges by enhancing the richness and control of the generated data.
- PSO: Optimizes the exploration of the latent space, ensuring that the model can find the most diverse and high-quality samples.
- Fuzzy Logic: Introduces controlled randomness to the sampling process, helping to capture a range of possible outputs and avoid overfitting.
- Hierarchical Structure: Organizes the latent space into different levels, capturing both global and fine-grained details of motion.
- Conditional Inputs: Ensures that the latent space is aligned with specific conditions like emotions and intensity, allowing for controlled and targeted generation.
- Disentanglement: Aims to separate different factors in the data, such as emotion versus general motion, making it easier to manipulate and understand the latent space.
Compared to a standard VAE, this enhanced model significantly improves the generation of diverse and contextually appropriate outputs. By incorporating PSO, the model can explore the latent space more effectively, discovering a wider range of plausible motions and emotions. Fuzzy logic adds variability to the generated samples, making them more realistic and less deterministic. The hierarchical structure helps in better organizing the complex data, allowing the model to capture both high-level and detailed features. Conditional inputs provide a mechanism to control the emotional content of the generated samples, and disentanglement ensures that these features are cleanly separated, allowing for more interpretable and adjustable outputs.
The model processes input data by first encoding it into a latent space influenced by additional conditions such as emotion and intensity. During training, the encoder learns to map these conditions along with the motion data into a structured latent space. The decoder then reconstructs the motion data from these latent representations, ensuring that the output aligns with the given conditions. PSO optimizes the exploration of this space, while fuzzy sampling adds variability, resulting in outputs that reflect a range of realistic motions and emotional expressions. The hierarchical and conditional aspects help in generating nuanced body motions that correspond accurately to the specified emotional states, providing a rich and varied dataset for applications such as animation, virtual reality, and emotion recognition systems.